Synthesizing cloth wrinkles by CNN-based geometry image superresolution: Synthesizing cloth wrinkles by CNN-based geometry image SR

We propose a novel deep learning-based method, called mesh superresolution, to enrich low-resolution (LR) cloth meshes with wrinkles. A pair of low and high-resolution (HR) meshes are simulated, with the simulation of the HR mesh tracks with that of the LR mesh. The frame data are converted into geometry images and used as a training data set. A residual network, called SR residual network, is employed to train an image synthesizer that superresolves an LR image into an HR one. Once the HR image is converted back to an HR mesh, it is abundant in wrinkles compared with its coarse counterpart. The synthesizing is very efficient and is 24x faster than a full HR simulation. We demonstrate the performances of mesh superresolution with various simulation scenes.